# Complete Blood Count–Derived Inflammation Indices to Predict 3-Year All-Cause Mortality in Patients With Diabetes and Acute Myocardial Infarction in Critical Care: Retrospective Cohort Study With Single-Center External Validation

## TL;DR

This study develops a model using blood-based inflammation markers to predict 3-year mortality in diabetic patients who had a heart attack, showing potential for clinical use.

## Contribution

A novel stacked predictive model using inflammation indices from routine blood tests to predict mortality in diabetic AMI patients.

## Key findings

- LMR was a protective factor against mortality, while NLR and PIV increased risk.
- The predictive model achieved an AUC of 0.803 in training and 0.781 in external validation.
- Lower LMR and higher NLR, NPR, PLR, and PIV were linked to higher mortality risk.

## Abstract

Inflammation plays a pivotal role in the progression of diabetes and its cardiovascular complications, particularly acute myocardial infarction (AMI). Patients with AMI often face high mortality and morbidity, making accurate prognosis crucial for clinical decision-making and outcome improvement.

This study aims to develop and validate a stacked predictive model using inflammation-related indices to predict 3-year all-cause mortality among patients with severe diabetes who experience acute myocardial infarction (AMI), aiming to improve patient prognosis.

We included 833 patients with severe diabetes combined with AMI from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (training/test cohort) and 166 cases from Zhongnan Hospital, China (external validation cohort with 3-year follow-up). A total of 5 inflammation-related indices (lymphocyte-to-monocyte ratio [LMR], neutrophil-to-lymphocyte ratio [NLR], neutrophil-to-platelet ratio [NPR], platelet-to-lymphocyte ratio [PLR], prognostic inflammatory value [PIV]) were analyzed for their association with mortality using Cox proportional hazards models. Kaplan-Meier curves and restricted cubic spline analysis explored survival probabilities and dose-response relationships. A stacked predictive model incorporating these indices was constructed, and its performance was evaluated using the area under the curve.

LMR was a protective factor (hazard ratio [HR] 0.44, 95% CI 0.25-0.77; P<.001), while NLR (HR 1.78, 95% CI 1.19-2.65; P=.004) and PIV (HR 1.59, 95% CI 1.09-2.30; P=.01) were associated with increased mortality risk. Kaplan-Meier analysis showed mortality increased with decreasing LMR quartiles and increasing NLR, NPR, PLR, and PIV quartiles. Restricted cubic spline confirmed that decreasing LMR and increasing NLR, NPR, PLR, and PIV were associated with higher adverse event risk. The predictive model achieved an area under the curve of 0.803 (95% CI 0.736-0.871) in internal testing and 0.781 (95% CI 0.704-0.858) in the external validation cohort.

The stacked predictive model serves as a robust tool for predicting 3-year outcomes in patients with diabetes combined with AMI. Its reliance on routine, low-cost indices highlights its economic viability and potential for widespread clinical implementation, particularly for optimizing resource allocation in resource-limited settings. However, considering the single-center nature of the derivation data, future multicenter validation is essential to verify the model’s generalizability across different health care systems before establishing it as a standard policy for risk stratification.